International Journal on Future Revolution in Computer Science & Communication Engineering
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Design of Frequency Divider (FD/2 and FD 2/3) Circuits for a Phase Locked Loop
Get PDFThis paper reports on three design of Frequency Divider (FD/2) and Frequency Divider (FD 2/3) circuits. Tanner EDA tool developed on 130nm CMOS technology with a voltage supply of 1.3 V is used to build, model, and compare all circuits. For the FD/2 circuit, E-TSPC Pass Transistor logic uses 1.77 µW, whereas TSPC logic consumes 5.57 µW for the FD 2/3 circuit. It implies that the TSPC logic is the best solution since it meets the speed and power consumption requirements
Computational Intelligence Based Electronic Healthcare Data Analytics Using Feature Selection with Classification by Deep Learning Architecture
Get PDFEHRs (Electronic health records) are a source of big data that offer a wealth of clinical patient health data. However, because these notes are free-form texts, writing formats and styles range greatly amongst various records, text data from eHRs, such as discharge rapid notes, provide analysis challenges. This research proposed novel technique in electronic healthcare data analysis based on feature selection and classification utilizingDL methods. here the input is collected as input EH data, is processed for dimensionality reduction, noise removal. A public data pre-processing method for dealing with HD-EHR data is dimensionality reduction, which tries to minimize amount of EHR representational features while enhancing effectiveness of following data analysis, such as classification. The processed data features has been selected utilizingweighted curvature based feature selection with support vector machine. Then this selected deep features has been classified using sparse encoder transfer learning. the experimental analysis has been carried out for various EH datasets in terms of accuracy of 96%, precision of 92%, recall of 77%, F-1 score of 72%, MAP of 65
Development of Fuzzy Inventory Model under Decreasing Demand and increasing Deterioration Rate
Get PDFThis research study proposed an inventory model with both the time varying variable deterioration and demand rate under the fuzzy environment. Fuzzy set theory is generally consider with imprecision and uncertainty nature of quantitative coefficients. In this system, we assumed the linearly increasing and decreasing function of time for deterioration and demand respectively. In this research work, we discuss a fuzzy inventory model solving by signed distance method where demand follow time varying. 
Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique
Get PDFIn this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally
AI based Pilot Contamination Analysis for 5G MIMO based on Multi Antenna Routing Networks and Multi-User Pilot Scheduling
Get PDFPilot contamination, a type of inter-cell interference, limits performance of large multi-input multi-output (MIMO) antenna systems.A drawback that results in ineffective bandwidth use is the burden of pilots who must estimate the channel regularly due to the acquisition of channel state data for channel estimation.Thus, there is a trade-off between spectral efficiency (SE)as well as quantity of pilots needed to evaluate channel.This research proposes novel technique in pilot contamination analysis (PCA) for 5G network based on MIMO by multi antenna routing system. The main aim is to detect pilot contamination and enhance spectral efficiency of the network. Here pilot contamination is detected using multi-user pilot scheduling with convolutional adversarial training model. As a result, a security breach occurs when crucial information slips to Eve during downlink transmission.Ability of the legitimate user to maintain secrecy can be greatly enhanced by knowing of an active eavesdropper.We also analyse the likelihood of detection, the likelihood of a false alarm, and the likelihood of a detection error.Simulation results show that suggested strategy to find PCA is effective.the proposed technique attained SINR of 72% , spectral efficiency of 85%,normalized MSE of 73%,PCA detection accuracy of 95%
Matlab based Simulink Modelling and Performance Analysis of Free Space Optical Communication System: A Review
Get PDFFree Space Optical communication is extended and compatible technique with the radio frequency technology. FSO provides BW spectrum that is in terahertz besides this speed of the data transfer is very fabulous. Due to these advantages FSO is becoming very popular communication technology for satisfying the growing demand of bandwidth traffic mainly for the long-distance communication. FSΟ communication has achieve important attention between various researchers for more BW and to transmit data securely in various domains. There must be integrated environment for FSΟ system which have robust mechanism to overcome signal loss under turbulence condition varies from medium to strong. During data transmission BER must be minimized otherwise significant information may be lost. There are various mechanism to control BER against various parameters like path loss factor, distance and atmospheric condition. After investigation various research paper, a robus
A Comparison Analysis of Machine Learning Algorithms on Cardiovascular Disease Prediction
Get PDFPeople nowadays are engrossed in their daily routines, concentrating on their jobs and other responsibilities while ignoring their health. Because of their hurried lifestyles and disregard for their health, the number of people becoming ill grows daily. Furthermore, most of the population suffers from a disease such as cardiovascular disease. Cardiovascular disease kills 35% of the world's population, according to W.H.O. A person's life can be saved if a heart disease diagnosis is made early enough. Still, it can also be lost if the diagnosis is constructed incorrectly. Therefore, predicting heart disease will become increasingly relevant in the medical sector. The volume of data collected by the medical industry or hospitals, on the other hand, can be overwhelming at times. Time-series forecasting and processing using machine learning algorithms can help healthcare practitioners become more efficient. In this study, we discussed heart disease and its risk factors and machine learning techniques and compared various heart disease prediction algorithms. Predicting and assessing heart problems is the goal of this research
Numerical Simulation and Design of COVID-19 Disease Detection System Based on Improved Computing Techniques
Get PDFThe high demand for testing the sickness has led to a lack of resources at emergency clinics as the coronavirus epidemic continues. PC vision-based frameworks can be used to increase the productivity of Coronavirus localization. However, a significant amount of information preparation is needed to create an accurate and reliable model, which is currently impractical given the peculiar nature of the illness. One such model is for differentiating pneumonia cases by using radiographs, and it has achieved sufficiently high exactness to be used on patients. Various models are currently being used inside the medical services sector to order different illnesses. This proposal evaluates the benefit of using motion learning to broaden the presentation of the Coronavirus location model, starting from the premise that there is limited information available for Coronavirus ID. Infections that affect the human lungs include viral pneumonia caused by the coronavirus and other viruses. The World Health Organization (W.H.O.) proclaimed Covid a pandemic in 2020; the sickness originated in China and quickly spread to other countries. Early diagnosis of infected patients aids in saving the patient's life and prevents the infection's further spread. As one of the quickest and least expensive methods for diagnosing the condition, the convolutional neural organization (CNN) model is suggested in this research study to assist in the early detection of the infection using chest X-Beam images. Two convolutional brain organizations (CNN) models were created using two different datasets. The primary model was created for double characterization using one of the datasets that only included pneumonia cases and common chest X-Beam images. The second model made use of the information advanced by the primary model using move learning and was created for three class divisions on chest X-Beam images of cases with the coronavirus, pneumonia, and regular cases
Optimal Coverage in Wireless Sensor Network using Augmented Nature-Inspired Algorithm
Get PDF One of the difficult problems that must be carefully considered before any network configuration is getting the best possible network coverage. The amount of redundant information that is sensed is decreased due to optimal network coverage, which also reduces the restricted energy consumption of battery-powered sensors. WSN sensors can sense, receive, and send data concurrently. Along with the energy limitation, accurate sensors and non-redundant data are a crucial challenge for WSNs. To maximize the ideal coverage and reduce the waste of the constrained sensor battery lifespan, all these actions must be accomplished. Augmented Nature-inspired algorithm is showing promise as a solution to the crucial problems in “Wireless Sensor Networks” (WSNs), particularly those related to the reduced sensor lifetime. For “Wireless Sensor Networks” (WSNs) to provide the best coverage, we focus on algorithms that are inspired by Augmented Nature in this research. In wireless sensor networks, the cluster head is chosen using the Diversity-Driven Multi-Parent Evolutionary Algorithm. For Data encryption Improved Identity Based Encryption (IIBE) is used. For centralized optimization and reducing coverage gaps in WSNs Time variant Particle Swarm Optimization (PSO) is used. The suggested model's metrics are examined and compared to various traditional algorithms. This model solves the reduced sensor lifetime and redundant information in Wireless Sensor Networks (WSNs) as well as will give real and effective optimum coverage to the Wireless Sensor Networks (WSNs)
Cyber-Attack Detection in Autonomous Vehicle Networks by Energy Aware Optimal Data Transmission with Game Fuzzy Q-Learning based Heuristic Routing Protocol
Get PDFThe automotive sector has seen a dramatic transition due to rapid technological advancement. Network connection has improved, enabling the transfer of the cars' technologies from being fully machine- to software-controlled. Controller area network (CAN) bus protocol manages network for autonomous vehicles. However, due to the intricacy of data and traffic patterns that facilitate unauthorised access to a can bus and many sorts of assaults, the autonomous vehicle network still has security flaws as well as vulnerabilities. This research proposes novel technique in cyber attack detection in autonomous vehicle networks enhanced data transmission based optimization and routing technique. Here the autonomous vehicle network optimal data transmission has been carried out using energy aware lagrangian multipliers based optimal data transmission. The cyber attack detection has been carried out using fuzzy q-learning based heuristic routing protocol. The experimental results has been carried out based on optimal data transmission and attack detection in terms of throughput of 95%, PDR of 94%, End-end delay of 46%, energy efficiency of 96%, network lifetime of 95%, attack detection rate of 88%